Modeling application dependencies to identify operational risk

ABSTRACT

Methods and systems for managing security in a cloud computing environment are provided. Exemplary methods include: gathering data about workloads and applications in the cloud computing environment; updating a graph database using the data, the graph database representing the workloads of the cloud computing environment as nodes and relationships between the workloads as edges; receiving a security template, the security template logically describing targets in the cloud computing environment to be protected and how to protect the targets; creating a security policy using the security template and information in the graph database; and deploying the security policy in the cloud computing environment.

CROSS REFERENCE TO RELATED APPLICATIONS

The present continuation-in-part application claims the priority benefit of U.S. patent application Ser. No. 16/428,828 filed on May 31, 2019 and titled “Cloud Security Management,” which is hereby incorporated by reference. The present continuation-in-part application is related to U.S. patent application Ser. No. 17/133,451, filed on Dec. 23, 2020, and titled “Modeling Topic-Based Message-Oriented Middleware Within a Security System,” and the present continuation-in-part application is related to U.S. patent application Ser. No. 17/133,458, filed on Dec. 23, 2020, and titled “Modeling Queue-Based Message-Oriented Middleware Relationships in a Security System,” all of which are hereby incorporated by reference.

FIELD OF THE INVENTION

The present technology pertains to communications networks, and more specifically to security in enterprise datacenter and cloud computing environments.

BACKGROUND ART

The approaches described in this section could be pursued but are not necessarily approaches that have previously been conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.

Due to the extensive use of computer networks by enterprises, there has been a dramatic rise in network attacks, a proliferation of computer viruses, and a constant distribution of other types of malicious content that attempts to attack, infect, or otherwise infiltrate the computer networks. Attackers breach internal networks and public clouds to steal critical data. For example, attackers target low-profile assets to enter the internal network. Inside the internal network and public clouds, and behind the hardware firewall, attackers move laterally across the internal network, exploiting East-West traffic flows, to critical enterprise assets. Once there, attackers siphon off valuable company and customer data.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

The present disclosure is related to various methods and systems for cloud security management. Specifically, a method for cloud security management may comprise: gathering data about workloads and applications in the cloud computing environment; updating a database using the data, the database representing the workloads of the cloud computing environment as nodes and relationships between the workloads as edges; and identifying dependencies and relationships that might represent an operational or cyber risk.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by limitation, in the figures of the accompanying drawings, in which like references indicate similar elements and in which:

FIG. 1 is a simplified block diagram of a cloud computing environment, according to some embodiments.

FIG. 2 is a simplified block diagram of a system for cloud security management, according to various embodiments.

FIG. 3 depicts a simplified graph of a cloud computing environment, in accordance with some embodiments.

FIG. 4A shows another graph of a cloud computing environment and FIG. 4B depicts a graph of an application, in accordance with various embodiments.

FIG. 5 is a simplified flow diagram of a method for cloud security management, according to some embodiments.

FIG. 6 is a simplified block diagram of a computing system, according to various embodiments.

FIG. 7 depicts a simplified graph of a cloud computing environment, in accordance with some embodiments.

FIG. 8 shows an exemplary dependency risk report for a cloud computing environment, in accordance with some embodiments.

DETAILED DESCRIPTION

While this technology is susceptible of embodiment in many different forms, there is shown in the drawings and will herein be described in detail several specific embodiments with the understanding that the present disclosure is to be considered as an exemplification of the principles of the technology and is not intended to limit the technology to the embodiments illustrated. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the technology. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes,” and/or “including,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that like or analogous elements and/or components, referred to herein, may be identified throughout the drawings with like reference characters. It will be further understood that several of the figures are merely schematic representations of the present technology. As such, some of the components may have been distorted from their actual scale for pictorial clarity.

FIG. 1 shows cloud computing environment 100 including workloads 110 _(1,1)-110 _(X,Y), according to some embodiments. Cloud computing environment 100 provides on-demand availability of computer system resources, such as data storage and computing power. Cloud computing environment 100 can physically reside in one or more data centers and/or be physically distributed over multiple locations. Cloud computing environment 100 can be hosted by more than one cloud service, such as those provided by Amazon, Microsoft, and Google. Cloud computing environment 100 can be limited to a single organization (referred to as an enterprise cloud), available to many organizations (referred to as a public cloud,) or a combination of both (referred to as a hybrid cloud). Examples of public clouds include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).

Each of workloads 110 _(1,1)-110 _(X,Y) can be a unit of computing resource, such as a physical computing system (also referred to as a bare metal server), virtual machine, container, pod, and combinations thereof. A physical computing system is computer hardware and not a virtual computing system, such as a virtual machine and container. In addition to running operating systems and applications, physical computing systems can be the hardware that virtual computing systems run on.

A virtual machine provides a substitute for a physical computing system, including functionality to execute entire operating systems. Virtual machines are created and run by a hypervisor or virtual machine monitor (VMM). A hypervisor is computer software or firmware which can run on workloads 110 _(1,1)-110 _(X,Y). A hypervisor uses native execution to share and manage hardware, allowing for multiple environments which are isolated from one another, yet exist on the same physical computing system.

Containers are an operating system-level virtualization method for deploying and running distributed applications without launching an entire virtual machine for each application. Containers can look like physical computing systems from the point of view of programs running in them. Generally, a computer program running on an operating system can see all resources (e.g., connected devices, files and folders, network shares, CPU power, etc.) of that physical computing system. However, programs running inside a container can only see the container's contents and devices assigned to the container. A pod is a group of containers with shared storage and/or network resources, and a shared specification for how to run the containers.

A container is an instance of an image. An image can be a file, comprised of multiple layers, with information to create a complete and executable version of an application. Containers can be arranged, coordinated, and managed—including means of discovery and communications between containers—by container orchestration (e.g., Docker Swarm®, Kubernetes®, Amazon EC2 Container Service (ECS), Diego, Red Hat OpenShift, and Apache® Mesos™). In contrast to hypervisor-based virtualization, containers may be an abstraction performed at the operating system (OS) level, whereas virtual machines are an abstraction of physical hardware.

Typically, workloads 110 _(1,1)-110 _(X,Y) of cloud computing environment 100 individually and/or collectively run applications and/or services. Applications and/or services are programs designed to carry out operations for a specific purpose. By way of non-limiting example, applications can be a database (e.g., Microsoft® SQL Server®, MongoDB, Hadoop Distributed File System (HDFS), etc.), email server (e.g., Sendmail®, Postfix, qmail, Microsoft® Exchange Server, etc.), message queue (e.g., Apache® Qpid™, RabbitMQ®, etc.), web server (e.g., Apache® HTTP Server™, Microsoft® Internet Information Services (IIS), Nginx, etc.), Session Initiation Protocol (SIP) server (e.g., Kamailio® SIP Server, Avaya® Aura® Application Server 5300, etc.), other media server (e.g., video and/or audio streaming, live broadcast, etc.), file server (e.g., Linux server, Microsoft® Windows Server®, etc.), service-oriented architecture (SOA) and/or microservices process, object-based storage (e.g., Lustre®, EMC® Centera®, Scality® RING®, etc.), directory service (e.g., Microsoft® Active Directory®, Domain Name System (DNS) hosting service, etc.), and the like.

Physical computing systems and cloud computing environments are described further in relation to FIG. 6 .

FIG. 2 shows system 200 for cloud security management, according to some embodiments. System 200 includes controller 210. Controller 210 can receive streaming telemetry 275 from network logs 270 (also referred to herein as logs or network and middleware system logs), events 285 from cloud control plane 280, and inventory 295 from configuration management database (CMDB) 290.

Network and middleware system logs 270 can be data sources such as flow logs from cloud services 260 ₁-260 _(Z) (e.g., Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)), vArmour DSS Distributed Security System, Software Defined Networking (SDN) (e.g., VMware NSX and Cisco Application Centric Infrastructure (ACI)), monitoring agents (e.g., Tanium Asset and Falco), and the like. Generally, streaming telemetry 275 can be low-level data about relationships between applications. Streaming telemetry 275 can include 5-tuple, layer 7 (application layer) process information, management plane logs, and the like. 5-tuple refers to a set of five different values that comprise a Transmission Control Protocol/Internet Protocol (TCP/IP) connection: a source IP address/port number, destination IP address/port number and the protocol in use. Streaming telemetry can alternatively or additionally include a volume of data (i.e., how much data is or how many data packets are) exchanged between workloads (e.g., workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ) in a network, (dates and) times at which communications (e.g., data packets) are exchanged between workloads, and the like.

Cloud control plane 280 establishes and controls the network and computing resources within a cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ). Cloud control plane 280 can include interfaces for managing assets (e.g., launching virtual machines and/or containers, configuring the network, etc.) in a cloud computing environment. For example, cloud control plane 280 can include one or more instances of container orchestration, such as Docker Swarm®, Kubernetes®, Amazon EC2 Container Service (ECS), Diego, and Apache® Mesos™ By way of further non-limiting example, cloud control plane 280 can include VMware vSphere, application programming interfaces (APIs) provided by cloud services 260 ₁-260 _(Z), and the like.

Events 285 can include information about a container (and/or a pod) being created, having a state change, having an error, and the like. For example, when a container is created, information about the workload such as a service name, image deployed, and the like can be received in events 285. By way of further example, additional information from an image registry corresponding to the deployed image can be gathered by controller 210.

Configuration management database (CMDB) 290 can be a database of information about the hardware and software components (also known as assets) used in a cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ) and relationships between those components and business functions. CMDB 290 can include information about upstream sources or dependencies of components, and the downstream targets of components. For example, inventory 295 can be used to associate an application name and other information (e.g., regulatory requirements, business unit ownership, business criticality, and the like) with the workload (e.g., workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ) it is running on.

Streaming telemetry 275, events 285, and inventory 295 can be ingested by graph 220. Graph 220 normalizes information received in streaming telemetry 275, events 285, and inventory 295 into a standard data format and/or model, graph database 225. Graph database 225 uses a graph data model comprised of nodes (also referred to as vertices), which is an entity such as a workload (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ), and edges, which represent the relationship between two nodes. Edges can be referred to as relationships. An edge can have a start node, end node, type, and direction, and an edge can describe parent-child relationships, actions, ownership, and the like. In contrast to relational databases, relationships are (most) important in graph database 225. In other words, connected data is equally (or more) important than individual data points.

Conventionally, security management systems stored raw logs of each and every individual communication between workloads. The amount of data scaled linearly and consumed massive amounts of storage. In contrast, streaming telemetry 275, events 285, and inventory 295, graph 220 (FIG. 2 ) can be used by graph 220 to create and update graph (database) 300. The individual communications are not stored. In this way, graph database 225 is advantageously scalable. For example, graph database 225 for large cloud computing environments of 30,000-50,000 workloads can be stored in memory of a workload (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ).

FIG. 3 depicts (simplified) graph (database) 300 of a cloud computing environment, according to various embodiments. Graph 300 is a simplified example, purely for illustrative purposes, of a graph in graph database 225 (FIG. 2 ). Graph 300 can include three workloads (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ): node 310, node 330, and node 350. As shown in FIG. 3 , edge (relationship) 320 is between nodes 310 and 330; edge (relationship) 340 is between nodes 330 and 350; edge (relationship) 360 is between nodes 350 and 310.

Using streaming telemetry 275, events 285, and inventory 295, graph 220 (FIG. 2 ) can determine information 335 about node 330. By way of non-limiting example, information 335 can include an application name, application function, business organization (e.g., division within a company), realm (e.g., production system, development system, and the like), (geographic) location/zone, Recovery Time Objective (“RTO”), Recovery Point Objective (“RPO”) and other metadata. Moreover, using layer 7 information (when available), the name of the database can be determined.

Referring back to FIG. 2 , graph 220 can employ various techniques to manage entropy. In a cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ), entropy is change to the workloads (e.g., created and removed), communications among workloads (e.g., which workloads communicate with other workloads), applications and services provided in the network, and the like. Typically in a (closed) enterprise cloud, entropy is low. For example, after monitoring an enterprise cloud for one month, another month of monitoring will reveal little that is new.

On the other hand, a web server connected to the Internet will have high entropy, because the number of relationships (connections) to clients on the Internet (nodes) is huge and continues to grow. To protect the size of graph database 225, graph 220 can recognize when there is high entropy and summarize the nodes. For example, the vast (and growing) number of clients on the Internet is represented by a single “Internet” object with one edge to the web server node.

According to some embodiments, a new relationship can be created around a particular node in graph database 225, as streaming telemetry 275, events 285, and inventory 295 are processed by graph 220. Graph 220 (FIG. 2 ) can further re-analyze the edges (relationships) connected to the particular node, to classify what the particular node is. For example, if the node accepts database client connections from systems that are known to be application servers, then graph 220 may classify the node as a database management system (i.e., a certain group). Classification criteria can include heuristic rules. Graph 220 can use machine learning algorithms and measure how close a particular node is to satisfying conditions for membership in a group. Classification is described further in U.S. Pat. No. 10,264,025 issued Apr. 16, 2019, titled “Security Policy Generation for Virtualization, Bare-Metal Server, and Cloud Computing Environments,” which is hereby incorporated by reference for disclosure of classification.

Visualize 230 can visually present information from graph database 225 to users according to various criteria, such as by application, application type, organization, and the like. FIGS. 4A and 4B show example visual presentations 400A and 400B, respectively, in accordance with some embodiments.

Visualize 230 can visually organize information from graph database 225. In some embodiments, nodes that behave similarly can be clustered together (i.e., be put in a cluster). For example, when two nodes have similar edges (relationships) and behave in a similar fashion (e.g., run the same application, are associated with the same organization, and the like), the two nodes can be clustered together. Nodes that are clustered together can be visually presented as a shape (e.g., circle, rectangle, and the like) which denotes that there are a certain number of workloads fulfilling the same function, instead of presenting a shape for each workload in the cluster.

In various embodiments, visualize 230 can detect and present communities. Communities are workloads (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ) that have a close set of edges (relationships). The constituent workloads of a community do not have to be the same—they can each perform different functions, such as web server, database server, application server, and the like—but the workloads are densely connected. In other words, the nodes communicate with each other often and in high volume. Workloads in a community act collectively to perform an application, service, and/or business function. Instead of displaying a shape (e.g., circle, rectangle, and the like) for each of the hundreds or thousands of workloads in a community, the community can be represented by a single shape denoting the application performed, the number of constituent workloads, and the like.

Protect 240 can use information in the graph database 225 to design security policies. Security policies can implement security controls, for example, to protect an application wherever it is in a cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ). A security policy can specify what is to be protected (“nouns”), for example, applications run for a particular organization. A security policy can further specify a security intent (“verbs”), that is, how to protect. For example, a security intent can be to implement Payment Card Industry Data Security Standard (PCI DSS) network segmentation requirements (a regulatory requirement), implement security best practices for databases, implement a whitelist architecture, and the like. By way of further example, a security intent can be specified in a template by a user (responsible for system administration, security, and the like).

Nouns and verbs can be described in a security template. A security template can include logic about how to process information in graph database 225 relating to workloads having a particular label/selection (nouns). Labels can be provided by logs 270 (e.g., layer 7 information), cloud control planes 280 (e.g., container orchestration), and CMDB 290. Protect 240 uses a security template to extract workloads to be protected (nouns) from graph database 225. Protect 240 further applies logic in the security template about how to protect the workloads (verbs) to produce a security policy. In various embodiments, security templates are JavaScript Object Notation (JSON) documents, documents in Jinja (or Jinja2), YAML Ain't Markup Language (YAML) documents, Open Policy Agent (OPA) rules, and the like. Jinja and Jinja2 are a web template engine for the Python programming language. YAML is a human-readable data-serialization language. OPA is an open source, general-purpose policy engine that enables unified, context-aware policy enforcement. Security templates are described further in U.S. patent application Ser. No. 16/428,838 filed May 31, 2019, titled “Template-Driven Intent-Based Security,” which is hereby incorporated by reference for disclosure of generating a security policy using security templates.

Protect 240 can produce multiple security policies, each reflecting independent pieces of security logic that can be implemented by protect 240. In various embodiments, security policies are JavaScript Object Notation (JSON) documents which are described to a user (responsible for system administration, security, and the like) in natural language. A natural language is any language that has evolved naturally in humans through use and repetition without conscious planning or premeditation. Natural language can broadly be defined in contrast to artificial or constructed languages such as computer programming languages. The multiple security policies can be placed in an order of precedence to resolve potential conflicts. Visualize 230 can be used to visualize the security policy (or security policies), showing the workloads protected, permitted relationships, and prohibited relationships. Protect 240 can then be used to edit the security policy. For example, there can be a primary and backup server (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ). The backup server may have never been used and may not have the same edges (relationships) as the primary server in graph database 225. The security policy can be edited to give the backup server the same permissions as the primary server.

Protect 240 can validate a security policy. The security policy can be simulated using graph database 225. For example, a simulation can report which applications are broken (e.g., communications among nodes needed by the application to operate are prohibited) by the security policy, are unnecessarily exposed by weak policy, and the like. Security policy validation is described further in U.S. patent application Ser. No. 16/428,849, titled “Validation of Cloud Security Policies,” which is incorporated by reference herein for disclosure of security policy validation.

Protect 240 can test a security policy. Protect can use historical data in graph database 225 to determine entropy in the cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ). For example, when a cloud computing environment first starts up, there are initially numerous changes as workloads are brought online and communicate with each other, such that entropy is high. Over time, the cloud computing environment becomes relatively stable with few changes, so entropy becomes low. In general, security policies are less reliable when entropy is high. Protect 240 can determine a level of entropy in the cloud computing environment and produce a reliability score and recommendation for the security policy. Security policy testing is described further in U.S. patent application Ser. No. 16/428,858 filed May 31, 2019, titled “Reliability Prediction for Cloud Security Policies,” which is incorporated by reference herein for disclosure of security policy reliability prediction.

Protect 240 can deploy a security policy (or security policies). The security policy is deployed as needed in one or more cloud computing environments of cloud services 260 ₁-260 _(Z) (e.g., Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)), vArmour DSS Distributed Security System, VMware NSX, and the like). Protect 240 can provide the security policy to one or more of cloud drivers 250 ₁-250 _(Z). Cloud drivers 250 ₁-250 _(Z) maintain an inventory and topology (i.e., current state) of the workloads in the cloud computing environments hosted by cloud services 260 ₁-260 _(Z), respectively. Cloud drivers 250 ₁-250 _(Z) can use their respective inventory and topology to apply the security policy to the appropriate workloads, and respond immediately to changes in workload topology and workload placement.

Cloud drivers 250 ₁-250 _(Z) can serve as an interface between protect 240 (having a centralized security policy) and cloud services 260 ₁-260 _(Z). In other words, cloud drivers 250 ₁-250 _(Z) implement the security policy using the different facilities (e.g., application programming interfaces (APIs)) and capabilities available from cloud services 260 ₁-260 _(Z). For example, each of cloud services 260 ₁-260 _(Z) can have different syntax and semantics for implementing security controls. Moreover, each of cloud services 260 ₁-260 _(Z) can have different security capabilities (e.g., communications/connections between workloads can only be expressly permitted and not expressly prohibited), rule capacity (limit on the number of rules), optimization methods, and the like.

Cloud drivers 250 ₁-250 _(Z) can maintain the integrity of the security policy in the cloud computing environments hosted by cloud services 260 ₁-260 _(Z) (referred to as the “cloud”). Cloud drivers 250 ₁-250 _(Z) can check that the security policy actually deployed in the cloud is as it should be, using the security policy's JSON source. When the security policy deployed in the cloud does not comport with the centralized security policy—such as when a bad actor logs into one of the cloud services and removes all the security rules—the responsible cloud driver (of cloud drivers 250 ₁-250 _(Z)) can re-deploy the security policy and/or raise an operational alert. Where supported, cloud services 260 ₁-260 _(Z) can notify the respective cloud driver (of cloud drivers 250 ₁-250 _(Z)) of changes to the topology and/or configuration. Otherwise, the respective cloud driver (of cloud drivers 250 ₁-250 _(Z)) can poll the cloud service (cloud services 260 ₁-260 _(Z)) to ensure the security rules are in place.

As described above, a security policy can be pushed down to the cloud computing environments hosted by cloud services 260 ₁-260 _(Z) using cloud drivers 250 ₁-250 _(Z), respectively. Additionally or alternatively, as new data comes into graph 220 as network logs 270, events 285 from cloud control plane 280, and inventory 295, protect 240 can check the new data against the security policy to detect violations and or drift (e.g., change in the environment and/or configuration).

Protect 240 can dynamically update a security policy as changes occur in the cloud computing environments hosted by cloud services 260 ₁-260 _(Z). For example, when a container (or pod) is deployed by container orchestration, it can be given a label, and cloud control plane 280 reports a container is deployed (as event 285). Labels can be predefined to specify identifying attributes of containers (and pods), such as the container's application function. When the label corresponds to an attribute covered by an active (deployed) security policy, protect 240 can dynamically add the new container to the active security policy (as a target). For example, when a pod is deployed for a particular organization and there is an active policy for that organization, the new workload is added to the security policy. Similarly, when a container is killed, the workload is removed from the security policy. Dynamically updating security policy is described further in U.S. Pat. No. 9,521,115 issued Dec. 13, 2016, titled “Security Policy Generation Using Container Metadata,” which is hereby incorporated by reference for disclosure of dynamically updating security policy.

FIG. 5 shows method 500 for managing cloud security, according to some embodiments. Method 500 can be performed by system 200 (FIG. 2 ), including controller 210. Method 500 can commence at step 510 where data from a cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ) can be received. For example, graph 220 (FIG. 2 ) can receive streaming telemetry 275 from network logs 270, events 285 from cloud control plane 280, and inventory 295 from configuration management database (CMDB) 290.

At step 520, a graph database can be created or updated using the cloud data. For example, streaming telemetry 275, events 285, and inventory 295 (FIG. 2 ) can be normalized into a standard data format and stored in graph database 225.

At step 530, a visual representation of the cloud computing environment as modeled by the graph database can be provided. For example, visualize 230 (FIG. 2 ) can present a graph using data in graph database 225. In some embodiments, nodes (representing workloads in the cloud computing environment) can be clustered and/or placed in communities for visual clarity.

At step 540, a security template can be received. A security template can include logic about how to extract information from graph database 225 to identify workloads to be targets of a security policy. In addition, a security template can specify how the workloads are to be protected (e.g., security intent).

At step 550, a security policy can be created. For example, protect 240 can use the security template to extract information from graph database 225 (FIG. 2 ) to produce a security policy for the security intent of the security template.

At step 560, the security policy can be validated. For example, protect 240 (FIG. 2 ) tests the security policy against a historical data set stored in graph database 225. Protect 240 can generate a report around the risks and implications of the security policy being implemented.

At step 570, the security policy can be tested. For example, protect 240 (FIG. 2 ) can measure entropy and a rate of change in the data set stored in graph database 225 to predict, when the security policy is deployed, the cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ) will change such that applications and/or services will break (e.g., be prevented from proper operation by the security policy).

At step 580, the security policy can be deployed to the cloud computing environment (e.g., cloud computing environment 100 in FIG. 1 ). For example, cloud drivers 250 ₁-250 _(Z) can produce requests, instructions, commands, and the like which are suitable for and accepted by cloud services 260 ₁-260 _(Z) (respectively) to implement the security policy in the cloud computing environments hosted by cloud services 260 ₁-260 _(Z) (respectively).

Optionally at step 580, the security policy can be maintained. For example, cloud drivers 250 ₁-250 _(Z) can make sure the security policy remains in force at the cloud computing environment hosted by a respective one of cloud services 260 ₁-260 _(Z). Optionally at step 580, the security policy can be dynamically updated as workloads subject to the deployed security policy are deployed and/or killed.

Although steps 510-580 are shown in a particular sequential order, various embodiments can perform steps 510-580 in different orders, perform some of steps 510-580 concurrently, and/or omit some of steps 510-580.

FIG. 6 illustrates an exemplary computer system 600 that may be used to implement some embodiments of the present invention. The computer system 600 in FIG. 6 may be implemented in the contexts of the likes of computing systems, networks, servers, or combinations thereof. The computer system 600 in FIG. 6 includes one or more processor unit(s) 610 and main memory 620. Main memory 620 stores, in part, instructions and data for execution by processor unit(s) 610. Main memory 620 stores the executable code when in operation, in this example. The computer system 600 in FIG. 6 further includes a mass data storage 630, portable storage device 640, output devices 650, user input devices 660, a graphics display system 670, and peripheral device(s) 680.

The components shown in FIG. 6 are depicted as being connected via a single bus 690. The components may be connected through one or more data transport means. Processor unit(s) 610 and main memory 620 are connected via a local microprocessor bus, and the mass data storage 630, peripheral device(s) 680, portable storage device 640, and graphics display system 670 are connected via one or more input/output (I/O) buses.

Mass data storage 630, which can be implemented with a magnetic disk drive, solid state drive, or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit(s) 610. Mass data storage 630 stores the system software for implementing embodiments of the present disclosure for purposes of loading that software into main memory 620.

Portable storage device 640 operates in conjunction with a portable non-volatile storage medium, such as a flash drive, floppy disk, compact disk, digital video disc, or Universal Serial Bus (USB) storage device, to input and output data and code to and from the computer system 600 in FIG. 6 . The system software for implementing embodiments of the present disclosure is stored on such a portable medium and input to the computer system 600 via the portable storage device 640.

User input devices 660 can provide a portion of a user interface. User input devices 660 may include one or more microphones, an alphanumeric keypad, such as a keyboard, for inputting alphanumeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. User input devices 660 can also include a touchscreen. Additionally, the computer system 600 as shown in FIG. 6 includes output devices 650. Suitable output devices 650 include speakers, printers, network interfaces, and monitors.

Graphics display system 670 includes a liquid crystal display (LCD) or other suitable display devices. Graphics display system 670 is configurable to receive textual and graphical information and processes the information for output to the display device.

Peripheral device(s) 680 may include any type of computer support device to add additional functionality to the computer system.

Some of the components provided in the computer system 600 in FIG. 6 can be those typically found in computer systems that may be suitable for use with embodiments of the present disclosure and are intended to represent a broad category of such computer components. Thus, the computer system 600 in FIG. 6 can be a personal computer (PC), hand held computer system, telephone, mobile computer system, workstation, tablet, phablet, mobile phone, server, minicomputer, mainframe computer, wearable, or any other computer system. The computer may also include different bus configurations, networked platforms, multi-processor platforms, and the like. Various operating systems may be used including UNIX, LINUX, WINDOWS, MAC OS, PALM OS, QNX ANDROID, IOS, CHROME, and other suitable operating systems.

Some of the above-described functions may be composed of instructions that are stored on storage media (e.g., computer-readable medium). The instructions may be retrieved and executed by the processor. Some examples of storage media are memory devices, tapes, disks, and the like. The instructions are operational when executed by the processor to direct the processor to operate in accordance with the technology. Those skilled in the art are familiar with instructions, processor(s), and storage media.

In some embodiments, the computer system 600 may be implemented as a cloud-based computing environment, such as a virtual machine operating within a computing cloud. In other embodiments, the computer system 600 may itself include a cloud-based computing environment, where the functionalities of the computer system 600 are executed in a distributed fashion. Thus, the computer system 600, when configured as a computing cloud, may include pluralities of computing devices in various forms, as will be described in greater detail below.

In general, a cloud-based computing environment is a resource that typically combines the computational power of a large grouping of processors (such as within web servers) and/or that combines the storage capacity of a large grouping of computer memories or storage devices. Systems that provide cloud-based resources may be utilized exclusively by their owners or such systems may be accessible to outside users who deploy applications within the computing infrastructure to obtain the benefit of large computational or storage resources.

The cloud is formed, for example, by a network of web servers that comprise a plurality of computing devices, such as the computer system 600, with each server (or at least a plurality thereof) providing processor and/or storage resources. These servers manage workloads provided by multiple users (e.g., cloud resource customers or other users). Typically, each user places workload demands upon the cloud that vary in real-time, sometimes dramatically. The nature and extent of these variations typically depend on the type of business associated with the user.

It is noteworthy that any hardware platform suitable for performing the processing described herein is suitable for use with the technology. The terms “computer-readable storage medium” and “computer-readable storage media” as used herein refer to any medium or media that participate in providing instructions to a CPU for execution. Such media can take many forms, including, but not limited to, non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical, magnetic, and solid-state disks, such as a fixed disk. Volatile media include dynamic memory, such as system random-access memory (RAM). Transmission media include coaxial cables, copper wire and fiber optics, among others, including the wires that comprise one embodiment of a bus. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROM disk, digital video disk (DVD), any other optical medium, any other physical medium with patterns of marks or holes, a RAM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a Flash memory, any other memory chip or data exchange adapter, a carrier wave, or any other medium from which a computer can read.

Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to a CPU for execution. A bus carries the data to system RAM, from which a CPU retrieves and executes the instructions. The instructions received by system RAM can optionally be stored on a fixed disk either before or after execution by a CPU.

Computer program code for carrying out operations for aspects of the present technology may be written in any combination of one or more programming languages, including an object oriented programming language such as JAVA, SMALLTALK, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

FIG. 7 depicts (simplified) graph (database) 700 of a cloud computing environment, according to various embodiments. Graph 700 is a simplified example, purely for illustrative purposes, of a graph in graph database 225 (FIG. 2 ). Graph 700 can include three workloads (e.g., of workloads 110 _(1,1)-110 _(X,Y) in FIG. 1 ): node 710, node 730, and node 750. As shown in FIG. 7 , edge (relationship) 720 is between nodes 710 and 730; edge (relationship) 740 is between nodes 730 and 750; edge (relationship) 760 is between nodes 750 and 710.

Using streaming telemetry 275, events 285, and inventory 295, graph 220 (FIG. 2 ) can determine information 735 about node 730. By way of non-limiting example, information 735 can include an application name, application function, business organization (e.g., division within a company), realm (e.g., production system, development system, and the like), (geographic) location/zone, Recovery Time Objective (“RTO”), Recovery Point Objective (“RPO”) and other metadata. Moreover, using layer 7 information (when available), the name of the database can be determined.

According to various exemplary embodiments, within the graph 220 (FIG. 2 ) and within the application controller 210 (FIG. 2 ), all of the information about all of the systems within a network and how they communicate over direct network connections and/or over message-oriented middleware (“MOM”) is stored. Message-oriented middleware is software or hardware infrastructure supporting sending and receiving messages between distributed systems. MOM allows application modules to be distributed over heterogeneous platforms and reduces the complexity of developing applications that span multiple operating systems and network protocols. The middleware creates a distributed communications layer that insulates the application developer from the details of the various operating systems and network interfaces. APIs that extend across diverse platforms and networks are typically provided by MOM. This middleware layer allows software components (applications, Enterprise JavaBeans, servlets, and other components) that have been developed independently and that run on different networked platforms to interact with one another.

By interfacing with inventory systems such as a configuration management database (“CMDB”), which could be a ServiceNow® CMDB (for example, Node 710), an enterprise governance, risk and compliance (“GRC”) tool such as RSA Archer® (for example, Node 730), or a “home built” tool such as the CMDB tools that many banks have developed (for example, Node 750), there is what is known as “common metadata.” Common metadata may comprise compliance information. For example, a system may be considered to be a Payment Card Industry Data Security Standard (“PCI DSS”) Category 1 system, or the system may have certain operational service level objectives.

In one example, the system (for example, a Node) has a recovery time objective (“RTO”). This is the number of hours that a system needs to recover within in order to meet its service level agreement, its service level requirements, or its recovery point objective (“RPO”), which is the amount of time that a system can lose data. When there is a failure, if the RPO is zero, it means the system is not allowed to lose any data. For example, by mapping into the graph relationships between systems that are publishing data and systems that are consuming data, one may query whether or not there are risks or issues relating to the ability of a system to recover.

For example, Node 710 may send out information consumed by Node 730. Node 710 may have a recovery time objective of four hours. Node 730 may have a recovery time objective of one hour. Generally, organizations know what the recovery time objective is for individual components of their systems. But they have difficulty mapping it across the dynamic dependencies that have been created. This is a problem. In the case of Node 730, it needs to recover its data within an hour. Node 710, however, might not recover the data for four hours. This is known as a “toxic combination.”

According to the exemplary systems and methods herein, it has a standard set of policies to recognize where these mismatches occur. It can identify in advance problematic dependencies such as described herein.

As another example, if a system is labeled as being PCI DSS Level 1, there is a rule within the PCI DSS specification that it cannot be accessed directly from a system on the Internet. The graph will show if a system on the Internet is connecting to a system that is labeled as PCI DSS Level 1 and that it is a toxic combination. This can be prevented by writing a policy, a set of permissions, or triggering an alarm as soon as it occurs.

Another example of the ability to use this dependency mapping to find toxic combinations is if Node 730 has a recovery point objective for data loss of zero and Node 710 has a recovery point objective of one hour. Node 710 could lose data for an hour, however, Node 730 cannot afford to lose any data at all. Connecting together these dependencies based on metadata about operational requirements or compliance obligations means that the exemplary systems and methods described herein can immediately find these problems. In most cases, the application controller performs this function.

FIG. 8 shows an exemplary dependency risk report 800 for a cloud computing environment, in accordance with some embodiments.

In further exemplary embodiments, multiple dependencies may be analyzed within a dependency tree. It is not constrained to just an analysis of just two assets. As shown in dependency risk report 800, 90 internal workloads, 26 dependency workloads, and 2 unknown workloads comprising 2,374 relationships, 26 relationships per workload and 226 active rules were analyzed. Additionally, the exemplary systems and methods herein may be used with audits. Auditors require organizations to prove that they have the capability to ensure that their critical functions meet their business risk requirements. The output of this system can show when a mismatch occurs or when a violation occurs. For example, dependency risk report 800 shows four violations between two nodes (source and destination, respectively) where each violation is recovery time objective (“RTO”) mismatch of 1 to 4. For example, the source needs to recover its data in 1 hour, however, the destination might take up to four hours to recover its data. A violation can occur for a number of reasons. It could be because the needs of an application have changed, and/or it could be because there is a new dependency between applications.

In some exemplary embodiments, organizations can produce an audit trail to prove that they have continuous and immediate capability to understand when risk profiles change. They can also demonstrate that they have a means of preventing risk profiles from changing. For example, the exemplary systems and methods herein can prevent a relationship between an out of scope PCI DSS system and a properly functioning PCI DSS Level 1 system that stores and processes credit card information.

Many regulatory agencies feel that business continuity should not be focused only on the planning process to recover operations after an event, but it should include the continued maintenance of systems and controls for the resilience of operations. Resilience incorporates, prior to measures to mitigate disruptive events, the ability to evaluate an entity's recovery capabilities. The exemplary systems and methods herein can automatically mitigate any toxic combinations in a complex network. Rules can be generated to find where there are combinations of conditions that will not allow an organization to meet their resilience and operational risk requirements. In some cases, policies may be provided with the system upon acquisition. But equally, users can label the system with their own operational conditions or their own metadata and write a rule, which allows them to evaluate when a problematic combination of things exists.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

Aspects of the present technology are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present technology. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated. 

What is claimed is:
 1. A computer-implemented method for modeling application dependencies to identify operational risk, the method comprising: gathering, by a processor, data about applications and computing systems in a cloud computing environment or in an enterprise datacenter; updating, by the processor, a graph database using the data including the graph database representing relationships as mapped between the applications in the cloud computing environment or in the enterprise datacenter; using logic to analyze, by the processor, the data in the graph database to identify, based on the relationships, operational mismatches as mapped between operational requirements set for at least one application of the applications and operational requirements set for at least one further application of the applications in the cloud computing environment or in the enterprise datacenter; deploying, by the processor, an alert for the identified operational mismatches; creating an operations policy based on a difference between a recovery time objective and the recovery time objective on a dependent system; and validating the operations policy by simulating the operations policy using the graph database, the validating the operations policy comprising: determining a level of entropy in the cloud computing environment; and determining a reliability score and at least one recommendation for the operations policy based on the level of entropy.
 2. The computer-implemented method of claim 1, wherein the data further comprises common metadata.
 3. The computer-implemented method of claim 2, wherein the common metadata further comprises compliance information.
 4. The computer-implemented method of claim 3, wherein the compliance information further comprises operational service level objectives.
 5. The computer-implemented method of claim 4, wherein the operational service level objectives comprise a recovery point objective.
 6. The computer-implemented method of claim 5, further comprising creating the operations policy based on a difference between the recovery point objective and the recovery point objective on a dependent system.
 7. The computer-implemented method of claim 6, wherein the operations policy is based on a predetermined amount of minimum operational risk.
 8. The computer-implemented method of claim 7, wherein the operations policy is based on a predetermined amount of maximum operational risk.
 9. The computer-implemented method of claim 5, wherein the creating the operations policy includes: identifying targets in the cloud computing environment or in the enterprise datacenter in the graph database using labels associated with an operations template.
 10. The computer-implemented method of claim 1, wherein the cloud computing environment is hosted by a plurality of different cloud services, the different cloud services being at least one of a public cloud, private cloud, and on-premise data center.
 11. The computer-implemented method of claim 10, further comprising determining operational risk throughout the plurality of the different cloud services.
 12. A system for modeling application dependencies to identify operational risk, the system comprising: a processor; and a memory communicatively coupled to the processor, the memory storing instructions executable by the processor to perform a method comprising: gathering, by a processor, data about applications and computing systems in a cloud computing environment or in an enterprise datacenter; updating a graph database using the data, the graph database representing relationships as mapped between the applications in the cloud computing environment or in the enterprise datacenter; using logic to analyze the data in the graph database to identify, based on the relationships, operational mismatches as mapped between operational requirements set for at least one application of the applications and operational requirements set for at least one further application of the applications in the cloud computing environment or in the enterprise datacenter; deploying an alert for the identified operational mismatches; creating an operations policy based on a difference between a recovery time objective and the recovery time objective on a dependent system; and validating the operations policy by simulating the operations policy using the graph database, the validating the operations policy comprising: determining a level of entropy in the cloud computing environment; and determining a reliability score and at least one recommendation for the operations policy based on the level of entropy.
 13. The system of claim 12, wherein the data further comprises common metadata.
 14. The system of claim 13, wherein the common metadata further comprises compliance information.
 15. The system of claim 14, wherein the compliance information further comprises operational service level objectives.
 16. The system of claim 12, wherein the alert further comprises a human readable pdf file.
 17. The system of claim 12, wherein the alert further comprises a machine readable image.
 18. The system of claim 12, wherein the alert is deployed via an application programming interface. 